In [2]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv('covid_19_clean_complete.csv', parse_dates = ['Date'], index_col=None)
data.head()
Out[2]:
In [3]:
data.info()
In [4]:
data.rename(columns={'Date': 'date',
'Id': 'id',
'Province/State':'state',
'Country/Region':'country',
'Lat':'lat',
'Long': 'long',
'Confirmed': 'confirmed',
'Deaths':'deaths',
'Recovered':'recovered'
}, inplace=True)
data.head()
Out[4]:
In [5]:
data.head()
Out[5]:
In [6]:
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
from plotly.subplots import make_subplots
Worldwide¶
In [7]:
grouped = data.groupby('date')['confirmed','deaths','recovered'].sum().reset_index()
fig = px.line(grouped, x="date", y="confirmed", title="Worldwide Confirmed Cases Over Time")
fig.show()
fig = px.line(grouped, x="date", y="deaths", title="Worldwide Deaths Over Time")
fig.show()
fig = px.line(grouped, x="date", y="recovered", title="Worldwide Recovered Over Time")
fig.show()
In [8]:
grouped_china = data[data['country'] == "China"].reset_index()
grouped_china_date = grouped_china.groupby('date')['date', 'confirmed', 'deaths', 'recovered'].sum().reset_index()
grouped_italy = data[data['country'] == "Italy"].reset_index()
grouped_italy_date = grouped_italy.groupby('date')['date', 'confirmed', 'deaths', 'recovered'].sum().reset_index()
grouped_us = data[data['country'] == "US"].reset_index()
grouped_us_date = grouped_us.groupby('date')['date', 'confirmed', 'deaths', 'recovered'].sum().reset_index()
grouped_rest = data[~data['country'].isin(['China', 'Italy', 'US'])].reset_index()
grouped_rest_date = grouped_rest.groupby('date')['date', 'confirmed', 'deaths', 'recovered'].sum().reset_index()
grouped_india = data[data['country'] == "India"].reset_index()
grouped_india_date = grouped_india.groupby('date')['date', 'confirmed', 'deaths', 'recovered'].sum().reset_index()
In [10]:
plot_titles = ['China', 'Italy', 'USA', 'Rest of the World', 'India']
fig = px.line(grouped_china_date, x="date", y="confirmed",
title=f"Confirmed Cases in {plot_titles[0].upper()} Over Time",
color_discrete_sequence=['#F61067'],
height=500
)
fig.show()
fig = px.line(grouped_italy_date, x="date", y="confirmed",
title=f"Confirmed Cases in {plot_titles[1].upper()} Over Time",
color_discrete_sequence=['#91C4F2'],
height=500
)
fig.show()
fig = px.line(grouped_us_date, x="date", y="confirmed",
title=f"Confirmed Cases in {plot_titles[2].upper()} Over Time",
color_discrete_sequence=['#6F2DBD'],
height=500
)
fig.show()
fig = px.line(grouped_rest_date, x="date", y="confirmed",
title=f"Confirmed Cases in {plot_titles[3].upper()} Over Time",
color_discrete_sequence=['#FFDF64'],
height=500
)
fig.show()
fig = px.line(grouped_india_date, x="date", y="confirmed",
title=f"Confirmed Cases in {plot_titles[4].upper()} Over Time",
color_discrete_sequence=['#FFDF64'],
height=500
)
fig.show()
In [11]:
plt.figure(figsize=(10,10))
one = plt.plot(grouped_china_date['date'], grouped_china_date['confirmed'], label='China')
two = plt.plot(grouped_italy_date['date'], grouped_italy_date['confirmed'], label='Italy')
three = plt.plot(grouped_us_date['date'], grouped_us_date['confirmed'], label='USA')
four = plt.plot(grouped_india_date['date'], grouped_india_date['confirmed'], label='India')
plt.title('Confirmed cases')
plt.legend()
plt.show()
In [12]:
data['state'] = data['state'].fillna('')
temp = data[[col for col in data.columns if col != 'state']]
latest = temp[temp['date'] == max(temp['date'])].reset_index()
latest_grouped = latest.groupby('country')['confirmed', 'deaths', 'recovered'].sum().reset_index()
In [13]:
fig = px.choropleth(latest_grouped, locations="country",
locationmode='country names', color="confirmed",
hover_name="country", range_color=[1,5000],
color_continuous_scale="peach",
title='Countries with Confirmed Cases')
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [14]:
fig = px.choropleth(latest_grouped, locations="country",
locationmode='country names', color="recovered",
hover_name="country", range_color=[1,5000],
color_continuous_scale="algae",
title='Countries with Recovered Cases')
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [15]:
fig = px.choropleth(latest_grouped, locations="country",
locationmode='country names', color="deaths",
hover_name="country", range_color=[1,5000],
color_continuous_scale="aggrnyl",
title='Countries with Death Cases')
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [16]:
europe = list(['Austria','Belgium','Bulgaria','Croatia','Cyprus','Czechia','Denmark','Estonia','Finland','France','Germany','Greece','Hungary','Ireland',
'Italy', 'Latvia','Luxembourg','Lithuania','Malta','Norway','Netherlands','Poland','Portugal','Romania','Slovakia','Slovenia',
'Spain', 'Sweden', 'United Kingdom', 'Iceland', 'Russia', 'Switzerland', 'Serbia', 'Ukraine', 'Belarus',
'Albania', 'Bosnia and Herzegovina', 'Kosovo', 'Moldova', 'Montenegro', 'North Macedonia'])
europe_grouped_latest = latest_grouped[latest_grouped['country'].isin(europe)]
In [17]:
fig = px.choropleth(europe_grouped_latest, locations="country",
locationmode='country names', color="confirmed",
hover_name="country", range_color=[1,2000],
color_continuous_scale='portland',
title='European Countries with Confirmed Cases', scope='europe', height=800)
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [18]:
_ = latest_grouped.sort_values('confirmed', ascending=False)[:20][::-1]
fig = px.bar(_, x='confirmed', y='country',
title='Confirmed Cases Worldwide', text='confirmed', height=1000, orientation='h')
fig.show()
In [19]:
formated_gdf = data.groupby(['date', 'country'])['confirmed', 'deaths'].max()
formated_gdf = formated_gdf.reset_index()
formated_gdf['date'] = pd.to_datetime(formated_gdf['date'])
formated_gdf['date'] = formated_gdf['date'].dt.strftime('%m/%d/%Y')
formated_gdf['size'] = formated_gdf['confirmed'].pow(0.3)
fig = px.scatter_geo(formated_gdf, locations="country", locationmode='country names',
color="confirmed", size='size', hover_name="country",
range_color= [0, 1500],
projection="natural earth", animation_frame="date",
title='COVID-19: Spread Over Time', color_continuous_scale="portland")
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [20]:
formated_gdf = data.groupby(['date', 'country'])['confirmed', 'deaths'].max()
formated_gdf = formated_gdf.reset_index()
formated_gdf['date'] = pd.to_datetime(formated_gdf['date'])
formated_gdf['date'] = formated_gdf['date'].dt.strftime('%m/%d/%Y')
formated_gdf['size'] = formated_gdf['deaths'].pow(0.3)
fig = px.scatter_geo(formated_gdf, locations="country", locationmode='country names',
color="deaths", size='size', hover_name="country",
range_color= [0, 100],
projection="natural earth", animation_frame="date",
title='COVID-19: Deaths Over Time', color_continuous_scale="peach")
# fig.update(layout_coloraxis_showscale=False)
fig.show()